Join Iris Software, recognized as one of India's Top 25 Best Workplaces in the IT industry. We are a rapidly growing IT services company committed to being a trusted technology partner and a preferred employer for top professionals.

Our vision is to empower enterprise clients across financial services, healthcare, transportation, and professional services with cutting-edge technology solutions. We specialize in complex, mission-critical applications leveraging Data & Analytics, Cloud, DevOps, and AI/ML technologies.

At Iris, we believe in fostering a culture where employees "Build Your Future. Own Your Journey." We provide a launchpad for growth through challenging projects, personalized career development, continuous learning, and mentorship, ensuring both personal and professional advancement.

Architect and design scalable Big Data solutions using technologies like Apache Spark, Hadoop, and Azure Databricks. Define robust data processing architectures, transformation strategies, and engineering standards aligned with business goals.

Lead the development of distributed data processing pipelines using Spark and Hadoop ecosystem components. Optimize SQL and Hive-based solutions for enhanced performance and scalability. Design and implement Data Lakehouse solutions with Apache Hudi or Apache Iceberg.

Establish comprehensive data ingestion, transformation, validation, and reconciliation frameworks to ensure data reliability. Drive performance tuning for Spark jobs, Databricks workloads, and Hadoop environments. Mentor team members on best practices in Big Data engineering and specific technologies.

We are seeking a Senior Big Data Engineer with 5-8 years of experience. Essential skills include Azure Databricks, Data Ingestion Tools (Sqoop), Hadoop (HDFS + YARN), Hadoop Ecosystem Fundamentals (HBase + Impala), Scala, Cloudera, SQL, and Apache Iceberg.

Key responsibilities involve designing and leading the development of scalable Big Data solutions, defining data processing architectures, and optimizing performance across various Big Data components. A strong understanding of data Lakehouse concepts and data reliability frameworks is crucial.

Demonstrate strong ownership, effective collaboration with stakeholders, and a commitment to quality-focused engineering. Apply strong analytical thinking and adaptability to evolving technologies and business requirements. Excellent communication and attention to detail are expected.